PREDICTIVE MAINTENANCE USING ML TO OPTIMIZE PLANT EFFICIENCY AND REDUCE EMISSIONS
Keywords:
Predictive maintenance, Machine learning, Emission reductionAbstract
In the modern industrial landscape, the integration of predictive maintenance (PdM) using machine learning (ML) has become essential for optimizing plant efficiency and minimizing emissions. This paper explores the transformative potential of predictive maintenance, which leverages data-driven insights to anticipate equipment failures and facilitate timely interventions. By transitioning from traditional maintenance strategies—reactive and preventive—to a proactive approach, organizations can significantly reduce unplanned downtime and enhance operational performance. The study reviews the historical development of predictive maintenance methodologies, highlights current trends in ML applications, and presents case studies demonstrating successful implementations across various industries. The findings reveal that predictive maintenance not only improves equipment reliability and operational efficiency but also contributes to substantial reductions in emissions, thereby promoting sustainable industrial practices. A comprehensive framework for implementing predictive maintenance using machine learning techniques is proposed, emphasizing the importance of data collection, preprocessing, and model development. The paper concludes with a call to action for industries to adopt predictive maintenance solutions, fostering collaboration between academia and industry for future advancements.References
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